O+ and OH- Transport through Confined Environments using Machine Learning and an Order-N Framework for Condensed-Phase Hybrid Density Functional Theory*

By including a fraction of exact exchange (EXX), hybrid functionals reduce the self-interaction error in semi-local density functional theory (DFT), and thereby furnish a more accurate and reliable description of the electronic structure in systems throughout chemistry, physics, and materials science. However, the high computational cost associated with hybrid DFT limits its applicability when treating large-scale and complex condensed-phase systems. To overcome this limitation, we have devised a highly accurate and linear-scaling (order-N) approach based on a local representation of the occupied space that exploits sparsity when evaluating the EXX interaction in real space, and recently extended this framework to treat heterogeneous systems without the need for system-dependent parameters. In this work, we use this approach to generate high-quality training data at the dispersion-inclusive hybrid DFT level for training a reactive machine-learned potential to study how confinement affects the diffusion of H₃O+(aq) and OH-(aq) at experimentally relevant length and time scales. To enable such large-scale data generation, we have performed a comprehensive overhaul of our software to exploit next-generation high-performance computing architectures, including a three-pronged strategy to improve the computation (including GPU acceleration), communication, and workload balance. With these developments, this work brings us closer to understanding H₃O+/OH- transport through confined aqueous environments, which is of fundamental importance in the energy sciences (e.g., transport/conductivity in alkaline fuel cells).